Saturday, August 26, 2017

Walt Disney Studios - The Innovation Continues!

It's another exclusive day at Miraikan! We got free passes to Miraikan special exhibition  "Art of Disney - The Magic of Animation" (Thanks to TIEC #TIECrox! :D).

Initially, we were under the impression that we are going to see lot of cartoon sketches and not more than that. However, soon we realized that we were wrong. Walt Disney Studios have demonstrated their journey from 1923 to 2017 in a way that we were so amazed on the sheer effort and attention to detail they have placed to keep the innovation alive in each and every production they embarked upon.  Here's few examples that impressed me.

Pinocchio - They've used multi plane cameras to add dimensionality (depth or 3D effects) as a visual effect

Bambi - They have studied animal anatomy (of deers) and they have used live animals as reference (There has been few deers, so that artists can observe their moves and behavior), Further, they have used minimalistic ink to depict forests.

"Always as you travel assimilate the sounds and sights of the world" - Walt Disney

Saludos Amigos - Before they produce animated films in diverse range of settings, they observe locations, societies, cultures, prominent shapes and colors during field visits to influence the "feel" of the final work. Observing the unique South American colors for "Saludos Amigos" is one such example. Similarly, they have observed Japanese culture for "Big Hero 6" and Africa for "Jungle book"

Fantasmic - Creative visual effects itself won't make the experience of the audience complete as it would address only one human sense. In Fantasmic, they have introduced the concept of visualization of sounds of classical music.

Dumbo - Dumbo is an elephant who doesn't talk. So, they have used effective expressions of emotions to convey its feelings to the audience.
Sad dumbo

Happy Dumbo with Opened ears and bright eyes

Lady and the Tramp - In Lady and the tramp, scenes are viewed as how a dog sees the world. (Few centimeters above the ground. (Dog's eye view)

How a dog sees the world? - A Dog's eye view scene

Frozen - Physical properties of snow (snow effects) has been considered in the animation movie for snow simulation in scenes (As given in the video below)  


Zootopia - They have analyzed animal hair and fur in different animal parks to get realistic look for their own animal characters. 

Animal fur reference. Source:

101 Dalmations - In this animated movie, they have used Xerox copying technology to animate many similar looking dogs. More information on that here.

Now a days, animations can be developed vastly with advanced computer graphics technologies and Disney Studios continue to strive on pushing the boundaries of imaginations as they used to be!

Wednesday, August 2, 2017

Neuroscience inspired Computer Vision


Having read the profound master piece “When breath becomes air”, by Neuroscientist – surgeon Paul Kalanithi, I was curious about how neuroscience could contribute to AI (Computer vision in particular). 

Then, I found an comprehensive article in Neuron Review journal (written by Demis Hassabis, Dharshan Kumaran, Christopher Summerfield, Matthew Botvinick) titled “Neuroscience inspired Artificial Intelligence”.  Here goes a brief excerpt of concepts I found inspiring in that article, related to computer vision.

  • How visual input is filtered and pooled into simple and complex areas of cells in area V1in visual cortex
  • Hierarchical organization of mammalian cortical systems 
Object recognition 
  • Transforming raw visual input into increasingly complex set of features - To achieve invariance towards pose, illumination and scale
  • Visual attention shifts strategically among different objects (no equal priority for all objects) - To ignore irrelevant objects in a given scene in the presence of a clutter, multi object recognition, image to caption generation, generative models to synthasize images 
Intuitive understanding of physical world 
  • Interpret and reason about scenes by decomposing them into individual objects and their relations 
  • Redundency reduction (encourages the emergence of disentangled representations of independent factors such as shape and position) - To learn objectness, construct rich object models from raw inputs using deep generative models, E.g., Variational auto encoder 
Efficient Learning 
  • Rapidly learn new concepts from only a handful of examples (Related with Animal learning, developmental psychology) 
  • Characters challenge - distinguish novel instances of an unfamiliar hand written character from another - "Learn to learn”  networks
Transfer Learning
  • Generalizing or transferring generalized knowledge gained in one context to novel previously unseen domains (E.g., Human who can drive a car drives an unfamiliar vehicle) - Progressive networks 
  • Neural coding using Grid codes in Mammalian entorhinal cortex - To formulate conceptual representations that code abstract, relational information among patterns of inputs (not just invariant features) 
Virtual brain analytics 
  • Increase the interpretability of AI computations, Determine response properties of units in a neural networks 
  • Activity maximization - To generate synthetic images by maximizing the activity of certain classes of unit 
From AI to neuroscience
  • Enhancing performances of CNNs has also yielded new insights into the nature of neural representations in high-level visual areas. E.g., 30 network architectures from AI to explain the structure of the neural representations observed in the ventral visual stream of humans and monkeys